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5 Jul 2006: Evaluation on held-out data (eval03)– 6 hours of test data– decoded using LVCSR trigram language model– baseline using confusion network decoding. ... φcn(O; λ) =[ F(ω1) F(ω2). φ(O; λ). ]. • Incorporating in score-space requires consistency

3 Apr 2006: The trees are built in a simple, greedy fashion, wherenon-terminal node tests are chosen from a set of candidatefeatures together with a set of candidate thresholds to max-imise the ... and used for training wereclean, single-instance images, and so our

9 Dec 2006: and probabilitiesFind Ngram rules. human transcriptionand diarisation. (optional)assign categories. test datatraining data. ... Rules whose probability ex-ceeds a threshold are then applied to the test data.

18 Sep 2006: create a set of such images for their tests in [7]), avideo taken of the object in its environment (e.g. ... The first setting was used as training, with the others used as test sets.

5 Jun 2006: The secondtest set was based on the 1000 word DARPA Resource Management test set. ... 71. 8.1 Average SNR for the RM test sets adding Lynx Helicopter noise attenuatedby 20dB.

21 Sep 2006: This meanswe can start from an initially small model and test the ‘rele-vance’ of each new input vectori sequentially.

22 Nov 2006: 3. INCREMENTAL BAYESIAN ADAPTATION. The Bayesian adaptation discussed in section 2 runs in a batchmode where all test data are assumed to be available before adap-tation. ... Theperformance was evaluated on the 2003 evaluation test dataset,eval03,

22 Nov 2006: Thus, the final fea-ture vector has 42 dimensions. Results are given on two test sets:dev04f consists of 0.5 hours of CCTV data from shows broad-cast in November ... Thiseffect is seen for both complementary models, on both test sets.

22 Nov 2006: Table 1: Clean, matched andSPLICE on AURORA 2.0 test set A,averaged across N1-N4, WER(%). ... M-Joint1 2.43 3.82 6.97 17.1416 1.95 2.80 4.23 9.89. Table 2: Model-basedJoint systems’ performance on AURORA2.0 test set A, averaged

21 Sep 2006: We used 18randomly selected training/test combinations for reporting identification rates. Comparative Methods. ... 0.9. 1. Dimension. Iden. tific. atio. n ra. te. Effect of the dimension on the test set.